一种基于样点代表性等级的土壤采样设计方法

采样设计是获取土壤空间分布信息的关键环节,直接影响到土壤制图的精度。目前常用的采样设计方法大多存在着设计样本量大、采样效率不高的问题。当可投入资源难以完成一次性大量采样时,采样往往需要多次、分批进行。然而现有分批采样方法多考虑各批采样点在地理空间的互补性,可能造成样本点在属性空间的重叠,影响采样资源的高效利用。鉴于此,本研究通过对与土壤在空间分布具有协同变化的环境因子进行聚类分析,寻找可代表土壤性状空间分布的不同等级类型的代表性样点,建立一套基于代表性等级的采样设计方法。将该采样方法应用于位于黑龙江省嫩江县鹤山农场的研究区,利用所采集的不同代表性等级的样点进行数字土壤制图并进行验证,探讨采样方案与数字土壤制图精度的关系,以评价本文所提出的采样方法。结果表明,通过代表性等级最高的少量样点可获取研究区的大部分主要土壤类型(中国土壤系统分类的亚类级别),且制图精度较高;随着代表性等级较低样点的加入,土壤图精度提高;但当样点增加到一定数量时,土壤图的精度变化不大。因此,与样点数相比,样点的代表性高低对制图精度的影响更大。该方法所提出的代表性等级可以为样点采集顺序提供参考,有助于设计高效的逐步采样方案。     

样点代表性等级采样法在丘陵山区土壤表层有机质制图中的应用

基于样点代表性等级的土壤采样方法在成本、应用性、制图准确度上具有明显的优势,但在其他方面(如敏感性)上仍需要大量研究。为了进一步研究这种方法的可用性,本文以安徽宣城境内的丘陵山区为研究区,应用该方法分析以往土壤采样点的代表性等级,进而研究样点代表性等级对土壤制图准确度的影响。研究结果表明:①高等级代表性链广泛存在,而低等级代表性链则较少;②代表性链的等级有效地从不同程度反映出土壤形成环境的变异;③样点代表性采样设计的采样点与规则化网格采样、目的性采样有很大不同;④一般地,随着低等级样点的逐渐加入,制图准确度增加,但增幅随着样点等级的降低而降低。这些结果说明,样点代表性等级采样法在应用、成本、准确度几个方面都有明显的优点,因而具有较好的应用前景。需要注意的是,在应用该方法选择样点时,样点的代表性应达到一定级别,以避免制图准确度不会因为样点的加入而降低。此外,由于其他地形地貌类型(如平原区)还缺乏较好的土壤协同环境因子,该方法的应用受到了一定程度的限制。     

面向数字土壤制图的土壤采样设计研究进展与展望

全球化土壤环境问题的出现对基础输入数据的精度、尺度和时序提出了更高要求,面向数字土壤制图的土壤采样研究得到了快速发展。首先利用文献计量学的方法定量化分析国内外土壤采样研究学科分布和研究热点变化;随后重点梳理了国内外土壤采样研究的文献,根据不同的土壤调查目的、调查区历史采样点将土壤采样设计分为:土壤全面采样设计、土壤补充采样设计、土壤验证采样设计和土壤监测采样设计;最后介绍了基于样点的推理制图方法。在此基础上,对未来在多尺度的土壤采样设计、土壤–环境因子关系的新型假设和采样设计中现实问题的量化等方面进行了展望,旨在为数字土壤调查工作的开展提供参考依据。     

基于样点个体代表性的大尺度土壤属性制图方法

大空间尺度范围的土壤属性分布信息是陆地表层过程模拟的基础信息.基于野外样点进行空间插值是获得土壤属性空间分布信息的重要手段.现有的空间插值方法通常要求所用样点对研究区土壤属性空间分布规律具有良好的全局代表性.然而,受采样经费和野外采样条件的限制,所采集的样点往往难以全面地反映研究区土壤属性的空间分布规律.基于这样的样点用现有空间插值方法得到的土壤属性分布图通常精度较低,并且由样点全局代表性差带来的推测不确定性也无法得到度量.为了合理利用这些已采集的但全局代表性不好的样点,本文提出了基于样点“个体代表性”推测土壤属性空间分布并度量推测不确定性的方法.该方法在两点环境条件越相似、土壤属性就越相似的假设下,认为每一样点可以代表与其环境条件相似的地区,并且代表程度可以由两点的环境相似度度量;通过分析环境相似度计算推测不确定性,并以环境相似度为权重计算样点可代表地区的土壤属性值.将该方法应用于推测新疆伊犁地区土壤表层有机质含量,经验证,本文方法能够有效地利用全局代表性差的样点推测样点能够代表地区的土壤属性空间分布,并且所得的推测不确定性与预测残差呈现正向关系,能够有效地指示推测结果的可靠程度.     

数字土壤制图的推理方法对比研究

数字土壤制图是基于计算机技术利用现代空间分析方法获取详细空间属性的土壤制图技术,是当下信息时代对土壤资源类型、数量以及空间分布进行详尽认识的新方法,而推理方法决定了制图效率和结果的可靠性。不同的研究区域必须与推理方法相互匹配,为研究适合小尺度空间范围上的土壤制图方法,本研究选取母质类型、地形因子及遥感光谱指数,在原始土壤图基础上采用面积加权法布设采样点,选用决策树、支持向量机、随机森林三种推理方法获取土壤—环境知识,从而获得研究区的土壤类型空间分布图,并通过实地采样点数据验证比较三种方法的制图精度。研究结果表明:(1)总体上,每种算法得到的土壤类型空间分布结果与原始土壤图高度相似,但相较于原始土壤图,推理得出的土壤图细节信息更丰富;(2)实地采样点验证结果显示,随机森林分类模型的总体分类精度与Kappa系数均优于决策树与支持向量机分类模型,分类结果最佳;且对比三种分类算法推理得到的各土壤类型的用户精度和生产精度,随机森林算法也较决策树与支持向量机两种算法更优。本研究结果可为数字土壤制图中推理方法的选取提供参考。     

基于样点的数字土壤属性制图方法及样点设计综述

土壤剖面数据与土壤类型图按照某种原则进行连接是目前获取土壤属性空间分布信息的主要方法,这种传统的土壤属性制图方法以土壤专家的“经验”和手工描绘为基础,耗费资本高、生产周期长.数字土壤制图通过借鉴先进的空间信息处理技术和高分辨率地形数据的优势,能够快速地获取高精度、高分辨率的土壤属性空间变化信息,是一种精细、高效、经济的土壤属性制图技术.本文详细介绍了基于样点进行数字土壤属性制图的3种方法:①基于空间自相关的方法:②基于空间自相关和土壤-环境关系混合相关的方法:③基于土壤-环境关系的方法.同时,为保证样点能够全面地捕捉到研究区内土壤属性空间变异特征,以上3种方法都对样点的数量、分布或典型性提出了较为严格的要求,即样点应具有全局代表性.因此,如何设计样点成为数字土壤属性制图中的一个重要问题.依据样点设计过程中是否能够整合已有样点,本文将样点设计方案分为采样设计方案和补样设计方案两种,并对其分别进行了详细的综述.     

基于多时相遥感影像和随机森林算法的土壤制图

获取准确的土壤-环境关系是数字土壤制图的关键,目前遥感影像已作为环境因子应用于土壤-环境知识的建立过程,但单幅遥感影像所包含的光谱信息差异难以将不同土壤类型区分开来。因此本文提出了一种基于多时相遥感影像的土壤制图方法：选取红安县滠水河流域为研究区,以母质类型图、等高线数据和多时相哨兵二号遥感影像为基础,提取与土壤形成有关的环境因子,通过随机森林算法获取土壤-环境关系,预测研究区各土壤类型的空间分布并成图,利用野外实地分层采样点验证推理图的精度。结果表明：推理土壤图总体分类精度高达86%,与原始土壤图对比,各土壤类型的空间分布具有一定相似性,展现了更为详细的空间细节信息,该研究成果可为更新土壤图工作提供新方法。     

土壤专题图中采样点点位标识模型的构建

采样点图在直观展示已采集样点分布同时,也为确定补充采样点的位置提供了参考。制图时,通常以采样点编码在地图图面标识各个采样点,但由于受空间地理位置影响,采样点标识常呈无序排列,当地图中采样点密度及地图幅面较大时,读图时难以查找目标采样点。为实现制图后的采样点标识呈有序排列,构建了"土壤专题图中采样点位标识模型(Soil Sampling Point Labeling Model for Thematic Soil Maps:SAMPLA)",该模型主要有读图视区划分模型;土壤采样点位归属读图视区判定模型;土壤采样点顺序标识模型等3个子模型组成。基于Arc GIS 10.0,采用C#语言进行计算机编程,实现了SAMPLA。用1∶50 000国家标准分幅和县级地图的土壤采样点对模型进行验证,验证结果表明,模型可适用于不同类型分幅地图、不同比例尺,有助于实现规范化、批量化制图,提高了读图效率;但是读图视区划分方案对标识结果有一定影响;其他行业专业领域,如环境科学、水科学、地质科学等在制作类似于土壤采样点的点位图层时也可采用此模型。     

土壤制图中土壤类型配色模型构建与应用

在全国1∶5万土壤图集制图中,土壤类型的配色既需表现土类等高级类型的分布特征,也要表现土属等较低级类型的差别。我国土壤低级类型众多,且1∶5万基本比例尺图幅达2万余幅,采用传统人工设色方法进行土壤制图,不仅效率低,而且难以保持图幅间土壤颜色的协调一致性。针对这一技术难题,本研究采用图幅间相似配色方法和人机交互的设计思想,通过建立1个多层级管理色库、人工设置土壤类型的Q配色单元及其多个近似色系(色组),建立了Q配色单元的避让选色和区域土壤特征分析等5个组件模型,构建了土壤类型配色模型(SCO-Model)。该模型在大比例尺土壤制图中不仅反映了区域土壤的总体分布特征,也表达了土壤类型间的差异,特别是实现了大比例尺土壤制图中土壤类型的快速智能配色,大大提高了制图效率。     

基于多源环境变量和随机森林的橡胶园土壤全氮含量预测

土壤全氮与土壤肥力和土壤氮循环紧密相关。掌握土壤全氮详细的空间分布信息对提高土壤肥力管理效率和更好地了解土壤氮循环至关重要。该文以儋州国营橡胶园为研究区域,采集2511个土壤样品,利用随机森林(random forest,RF)、逐步线性回归(stepwise linear regression,SLR)、广义加性混合模型(generalized additive mixed model,GAMM)以及分类回归树(classification and regression tree,CART)结合多源环境变量(成土母质、平均降雨量、平均气温和归一化植被指数)对研究区橡胶园土壤全氮含量进行空间预测,并通过754个独立验证点比较了4种模型的预测精度。结果表明RF对土壤全氮的预测值和实测值的相关系数(0.82)明显高于SLR(0.68)、GAMM(0.70)和CART(0.69),而RF的预测平均绝对误差(0.08836 g/kg)和均方根误差(0.13090 g/kg)均低于SLR、GAMM和CART。此外,RF模型预测结果能反映更为详细的局部土壤全氮含量空间变化信息,与实际情况更为接近。可见,RF模型可作为橡胶园土壤全氮含量空间分布预测的高效方法,为其他土壤属性的空间分布预测提供了一种新的方法。     

结合多等级代表性采样和模糊隶属度的区域土壤制图方法

High-resolution and detailed regional soil spatial distribution information is increasingly needed for ecological modeling and land resource management. For areas with no point data, regional soil mapping includes two steps: soil sampling and soil mapping. Because sampling over a large area is costly, efficient sampling strategies are required. A multi-grade representative sampling strategy, which designs a small number of representative samples with different representative grades to depict soil spatial variations at different scales,could be a potentially efficient sampling strategy for regional soil mapping. Additionally, a suitable soil mapping approach is needed to map regional soil variations based on a small number of samples. In this study, the multi-grade representative sampling strategy was applied and a fuzzy membership-weighted soil mapping approach was developed to map soil sand percentage and soil organic carbon(SOC) at 0–20 and 20–40 cm depths in a study area of 5 900 km2 in Anhui Province of China. First, geographical sub-areas were delineated using a parent lithology data layer. Next, fuzzy c-means clustering was applied to two climate and four terrain variables in each stratum. The clustering results(environmental cluster chains) were used to locate representative samples. Evaluations based on an independent validation sample set showed that the addition of samples with lower representativeness generally led to a decrease of root mean square error(RMSE). The declining rates of RMSE with the addition of samples slowed down for 20–40 cm depth, but fluctuated for 0–20 cm depth. The predicted SOC maps based on the representative samples exhibited higher accuracy, especially for soil depth 20–40 cm, as compared to those based on legacy soil data. Multi-grade representative sampling could be an effective sampling strategy at a regional scale. This sampling strategy, combined with the fuzzy membership-based mapping approach, could be an optional effective framework for regional soil property mapping. A more detailed and accurate soil parent material map and the addition of environmental variables representing human activities would improve mapping accuracy.     

A new mechanical soil sampling method

Abstract

In order to determine the nutrient level and other characteristics of the soil a new mechanical soil sampling machine has been constructed. The machine ellimates the errors caused by taking the samples manually and It speeds the sampling procedure as well. The sampling machine equipped with discs drawn by a tractor with hydraulics can work continuously. Specially designed spoons on the discs take the samples out of the soil at a given depth. Using two discs collection of parallel samples is possible. Shoots and stubbles on the field do not interfere or contaminate the samples. Using a double disced type machine for taking 2 average samples on every twelve hectares the output of the machine is 50–100 hectares/hour. A new evaluation method has been worked out for the calculation of the characteristics values of the field.     

面向土壤系统分类的土壤调查制图方法的初步研究

我国的土壤系统分类方案已经基本形成,但还没有相应的土壤调查方法技术研究。按照传统土壤调查的主要剖面、检查剖面、定界剖面的思想与办法,采用空间内插技术,在研究区的4条实验路线上共挖掘了64个剖面点,通过这些剖面点的诊断层和诊断特性确定了研究区的土壤类型并勾绘了土壤类型界线。再用1条检验路线对勾绘的土壤图进行检查,结果表明,在检验路线上设置的20个检查剖面点中,其中19个剖面点与实际情况相符,正确率95%,说明使用内插法进行面向土壤系统分类的土壤调查制图是可行的。再结合土壤景观可以辅助土壤调查工作者更高效地确定土壤类型、勾绘土壤界线。     

The effect of survey density on the results of geopedological approach in soil mapping: A case study in the Borujen region, Central Iran

Landscapes are considered to be complex systems that are hierarchically structured and spatially scale-dependent. Geopedology allows a systematic approach in geomorphic analysis for soil mapping that extrapolates the results obtained at sample areas up to similar units. This paper examines the influence of sampling intervals on the accuracy of geopedological results in the Borujen region, Central Iran. After a primary interpretation of the study area on air photos (1:20 000), a geomorphic unit that encompassed the maximum surface of the sample area (and also the study area) was selected and was surveyed at three different scales: 500, 250 and 125 m intervals. The credibility of generalization of the results of the geopedological approach for the studied unit was tested by comparison with three profiles in a similar unit outside the sample area, named the validation area. Although the type of mapping unit in the sample area and the validation area was the same (complex) at the three different scales, the dominant soil in the validation area was different in comparison with the sample area at 125 and 250 m intervals. The results indicate that the geopedological approach to soil mapping is better carried out in reconnaissance or exploratory surveys. Comparison of soil types between the unit in the sample area and the validation area at three scales indicated that by increasing the taxonomic accuracy, the hidden aspects of the soils may be more identifiable. Therefore, although the geopedological approach tries to distinguish more homogeneous soil mapping units, it still is not able to fully define and represent the variability and apparent chaotic nature of the soils. We recommend further investigations on different techniques of stratifying the landscape in order to better analyze and understand the soil forming processes and soil variability and to improve sampling and mapping approaches.     

精准农业土壤采样密度研究——以上海精准农业试验示范基地为例

通过土壤密集采样识别农田土壤物理生化性状的空间差异,构建土壤与农作物产量关系模型,用间接方法提高土壤性状信息的分辨率并定量评价这些信息的可靠性非常必要。在评价多种插值方法的基础上,对中国科学院上海精准农业示范基地每公顷3个样本、等距离系统抽样的280个土壤有机质样本采用克立格方法进行插值,估算样本之间土壤有机质含量和分析估算的可靠性,并与距离倒数函数插值法进行对比。对几个减少样本数量的方案进行分析,提出在满足精准水稻种植管理分区要求前提下可减少1/2原有土壤采样样本,并给出适于水稻精准种植土壤采样设计的逐步优化方法。     

Incorporating limited field operability and legacy soil samples in a hypercube sampling design for digital soil mapping

Most calibration sampling designs for Digital Soil Mapping (DSM) demarcate spatially distinct sample sites. In practical applications major challenges are often limited field accessibility and the question on how to integrate legacy soil samples to cope with usually scarce resources for field sampling and laboratory analysis. The study focuses on the development and application of an efficiency improved DSM sampling design that (1) applies an optimized sample set size, (2) compensates for limited field accessibility, and (3) enables the integration of legacy soil samples. The proposed sampling design represents a modification of conditioned Latin Hypercube Sampling (cLHS), which originally returns distinct sample sites to optimally cover a soil related covariate space and to preserve the correlation of the covariates in the sample set. The sample set size was determined by comparing multiple sample set sizes of original cLHS sets according to their representation of the covariate space. Limited field accessibility and the integration of legacy samples were incorporated by providing alternative sample sites to replace the original cLHS sites. We applied the modified cLHS design (cLHS_adapt) in a small catchment (4.2 km²) in Central China to model topsoil sand fractions using Random Forest regression (RF). For evaluating the proposed approach, we compared cLHS_adapt with the original cLHS design (cLHS_orig). With an optimized sample set size n = 30, the results show a similar representation of the cLHS covariate space between cLHS_adapt and cLHS_orig, while the correlation between the covariates is preserved (r = 0.40 vs. r = 0.39). Furthermore, we doubled the sample set size of cLHS_adapt by adding available legacy samples (cLHS_adapt+) and compared the prediction accuracies. Based on an external validation set cLHS_val (n = 20), the coefficient of determination (R²) of the cLHS_adapt predictions range between 0.59 and 0.71 for topsoil sand fractions. The R²‐values of the RF predictions based on cLHS_adapt+, using additional legacy samples, are marginally increased on average by 5%.     

基于有效含水量的土壤水分监测点布设的空间分层采样方法

为了优化灌溉实践,构建准确估计平均土壤水分的监测点布设准则,该研究引入有效含水量（Available Water Capacity, AWC）作为辅助变量,结合经典统计学和地统计学构建了一种基于辅助变量空间自相关的分层采样方法（Stratified Sampling Method Based on Spatial Autocorrelation of Auxiliary Variables,SSAV）,克服直接以土壤水分为变量时受其强时空变异影响的弊端,并在田块尺度进行试验。结果表明：0～40和0～80 cm土层的AWC服从正态分布;在90%置信区间,采样误差为10%时研究区内0～40和0～80 cm土层的监测点数目分别为7个和6个;基于SSAV布点法估计土壤水分的相对误差变化范围为–23.23%～35.15%,较简单随机布点（Simple Random Sampling,SRS）法减小了26.48%。标准差的平均值为4.78%,较SRS降低了17.30%。基于SSAV的0～40和0～80 cm两个土层的估计值和观测值之间的平均均方根误差RMSE为0.010 4 cm3/cm3,基于SRS的RMSE为0.012 0 cm3/cm3,显著性检验P<0.001,SSAV显著提高了对土壤水分的估计精度和准度。SSAV为获得区域平均土壤水分提供了省时、省力、低成本的监测点布设方案,为农业水资源管理和提升农业用水效率提供了保障。